To understand the impact of our dataset we first
showcase the
previous works that have been done in this domain.
We report some of the resources which are currently available for verbalizing answers. GENQA [1]} and COREQA [2]} are two datasets in Chinese language which provide answers in the form of sentences rather than a concise answer. To the best of our knowledge, VQuAnDa [3]} is the first and only dataset in English language to provide answer verbalization. The dataset contains questions from LC-QuAD [4]} on DBpedia as the target KG. The advantage of this dataset is doubled due to the availability of SPARQL representation for questions. Thus, it is also useful for model's verbalizing answers from a SPARQL query. The dataset was aimed to enable users to validate the information generated from QA systems by incorporating not only the retrieved information but also additional characteristics indicating the process of answer retrieval. Nevertheless, this dataset does not cover key features of the user question to mimic human conversation in addition to enabling answer validation.
